Evolutionary algorithms are especially well suited for multi-objective optimization.
According to the survey by [1]}, genetic algorithms (GAs) are among the most commonly applied techniques for multi-objective feature selection.
Inspired by natural evolution, GAs apply recombination and mutation operators to iteratively improve the population of solution candidates.
Through techniques like non-dominated sorting [2]}, [3]}, genetic algorithms have become a powerful tool for multi-objective optimization.
Several GA-based methods have been proposed for the task of wrapper-based feature selection [4]}, [5]}, [6]}.
